Activity Segmentation and Fish Tracking from Sonar Videos by combining Artifacts Filtering and a Kalman Approach

Segmentation-based object tracking using image warping and Kalman filtering

We propose a segmentation-based method of object tracking using image warping and Kalman filtering. The object region is defined to include a group of patches, which are obtained by a watershed algorithm. In a robust M-estimator framework, we estimate dominant motion of the object region. A linear Kalman filter is employed to predict the estimated affine motion parameters based on a second orde...

Monitoring process transitions by Kalman filtering and time-series segmentation

Time-series segmentation algorithms, such as methods based on Principal Component Analysis (PCA) and fuzzy clustering, are based on input-output process data. However, historical process data alone may not be sufficient for the monitoring of process transitions. Hence, the key idea of this paper is to incorporate the first-principle model based state estimation into the segmentation algorithm t...

Tracking and Kalman Filtering Made Easy

Visual Tracking by Adaptive Kalman Filtering and Mean Shift

A method for object tracking combining the accuracy of mean shift with the robustness to occlusion of Kalman filtering is proposed. At first, an estimation of the object’s position is obtained by the mean shift tracking algorithm and it is treated as the observation for a Kalman filter. Moreover, we propose a dynamic scheme for the Kalman filter as the elements of its state matrix are updated o...

A Kalman Filtering Perspective for Multiatlas Segmentation

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only co...